System and Method for Scheduling Connected Vehicles to Cross Non-Signalized Intersections

ABSTRACT

A method comprises receiving driving data from a plurality of connected vehicles approaching an intersection, the driving data comprising a speed and position of a connected vehicle, determining estimated times of arrival that each of the connected vehicles will arrive at the intersection based on the driving data, scheduling the connected vehicles to cross the intersection in a particular order based on the estimated times of arrival, and transmitting the scheduled order to the connected vehicles.

TECHNICAL FIELD

The present specification relates to a traffic management system andmore particularly to a system and method for scheduling connectedvehicles to cross non-signalized intersections.

BACKGROUND

As automobiles or other vehicles approach an intersection while drivingalong a road, vehicles must take into account other vehicles in order tocross the intersection. Many intersections have traffic signals tomanage vehicle traffic at the intersections. However, many otherintersections do not have traffic signals. At these intersections,vehicles must navigate the intersection without the assistance oftraffic signals.

Traffic rules and laws have been created to instruct drivers as to whichvehicles have the right of way when crossing an intersection withouttraffic signals. For human-driven, non-connected vehicles, drivers mayrely on these traffic rules and their own judgment and observation ofother vehicles to cross such an intersection. However, connectedvehicles, either human-driver or autonomous, may communicate with eachother and/or a traffic management server in order to negotiate anintersection. In particular, as a plurality of connected vehiclesapproach an intersection without traffic signals, the connected vehiclesmay be assigned an order to cross the intersection. The connectedvehicles may then cross the intersection in the assigned order, therebyavoiding any conflicts.

When a traffic management server assigns an order for connected vehiclesto cross an intersection, the order may be based on a First-In-First-Out(FIFO) algorithm. That is, the order for the connected vehicles to crossthe intersection may be based on the position of the vehicles (e.g., thedistance of each connected vehicle to the intersection). For example,the connected vehicle closest to the intersection may be assigned thefirst slot to cross the intersection and the connected vehicle that isfurthest from the intersection may be assigned the last slot to crossthe intersection. However, assigning an order for connected vehicles tocross an intersection in this manner fails to take into account vehiclespeeds and other factors and may lead to inefficiencies. For example, afirst vehicle that is slightly further away from an intersection than asecond vehicle may have a higher speed than the second vehicle. Thus, ifthe first vehicle is scheduled to cross the intersection after thesecond vehicle, the first vehicle may have to significantly reduce itsspeed as it approaches the intersection. Thus, there is a need for animproved system and method for scheduling connected vehicles to crossnon-signalized intersections.

SUMMARY

In an embodiment, a method may include receiving driving data from aplurality of connected vehicles approaching an intersection, determiningestimated times of arrival that each of the connected vehicles willarrive at the intersection based on the driving data, scheduling theconnected vehicles to cross the intersection in a particular order basedon the estimated times of arrival, and transmitting the scheduled orderto the connected vehicles. The driving data may include a speed andposition of a connected vehicle.

In another embodiment, a server may include a controller configured toreceive driving data from a plurality of connected vehicles approachingan intersection, determine estimated times of arrival that each of theconnected vehicles will arrive at the intersection based on the drivingdata, schedule the connected vehicles to cross the intersection in aparticular order based on the estimated times of arrival, and transmitthe scheduled order to the connected vehicles. The driving data mayinclude a speed and position of a connected vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments set forth in the drawings are illustrative and exemplaryin nature and not intended to limit the disclosure. The followingdetailed description of the illustrative embodiments can be understoodwhen read in conjunction with the following drawings, where likestructure is indicated with like reference numerals and in which:

FIG. 1 schematically depicts a system comprising a traffic managementserver, according to one or more embodiments shown and described herein;

FIG. 2 depicts a schematic diagram of a vehicle system, according to oneor more embodiments shown and described herein;

FIG. 3 depicts a schematic diagram of the traffic management server ofFIG. 1, according to one or more embodiments shown and described herein;

FIG. 4 illustrates potential conflict paths at a non-signalizedintersection, according to one or more embodiments shown and describedherein;

FIG. 5A illustrates an example acceleration profile of a vehicleapproaching an intersection, according to one or more embodiments shownand described herein;

FIG. 5B illustrates another example acceleration profile of a vehicleapproaching an intersection, according to one or more embodiments shownand described herein;

FIG. 6 illustrates an example scheduling of connected vehiclesapproaching an intersection, according to one or more embodiments shownand described herein;

FIG. 7 depicts a flowchart of a method for operating the trafficmanagement server of FIGS. 1 and 3, according to one or more embodimentsshown and described herein; and

FIG. 8 illustrates example simulation results.

DETAILED DESCRIPTION

The embodiments disclosed herein include a system and method forscheduling connected vehicles to cross a non-signalized intersection. Itis expected that the number of connected vehicles on the road (bothhuman driven and autonomous) will increase around the world in the nextseveral decades. A connected vehicle is able to communicate remotelywith systems outside of the vehicle (e.g., a traffic management serveror other vehicles).

As connected vehicles approach an intersection without traffic signals(referred to herein as a non-signalized intersection), the connectedvehicles may transmit driving information (e.g., the location and speedof the vehicle) to a traffic management server. The traffic managementserver may receive the driving information from a plurality of connectedvehicles approaching an intersection and may schedule the vehicles tocross the intersection using an enhanced FIFO algorithm, as disclosedherein.

In particular, the server may determine an estimated time of arrivalindicating a time that each connected vehicle is estimated to arrive atthe intersection. The server may then schedule each connected vehicle tocross the intersection in an order based on the estimated time ofarrival of each vehicle to the intersection. That is, the first vehicleestimated to arrive at the intersection may be scheduled to cross theintersection first, the next vehicle estimated to arrive at theintersection may be scheduled to cross the intersection second, and soon. The server may then transmit the scheduled order to the connectedvehicles. The connected vehicles may receive the scheduled order and mayadjust their driving behavior (e.g., adjusting a speed) accordingly suchthat the connected vehicles cross the intersection in the scheduledorder. While this disclosure refers to vehicles crossing anintersection, it should be understood that this term encompassesvehicles navigating an intersection in any manner (e.g., driving throughthe intersection, turning, etc.).

By scheduling connected vehicles to cross an intersection in an orderbased on estimated time of arrival, traffic efficiency may be improvedcompared with scheduling connected vehicles using other methods. Forexample, in other methods, connected vehicles may be scheduled to crossan intersection in an order based on a distance of each connectedvehicle to the intersection. That is, a first connected vehicle that iscloser to an intersection may be scheduled to cross the intersectionbefore a second vehicle that is further away from the intersection.However, if the second vehicle is driving faster than the first vehicle,this may require the second vehicle to significantly slow down in orderto allow the first vehicle to cross the intersection first. If instead,the vehicles are scheduled to cross the intersection based on theirestimated time of arrival, as disclosed herein, the second vehicle maybe scheduled to cross the intersection before the first vehicle, eventhough the second vehicle is initially further from the intersection. Assuch, the second vehicle may not need to slow down, thereby increasingtraffic flow and efficiency.

Turning now to the figures, FIG. 1 schematically depicts a system forscheduling connected vehicles to cross non-signalized intersections. Asystem 100 includes a traffic management server 102. The trafficmanagement server 102 may receive data from one or more connectedvehicles, as disclosed herein. In the example of FIG. 1, connectedvehicles 104, 106, 108, and 110 all approach a non-signalizedintersection 112. However, it should be understood that in otherexamples, the system 100 may operate with any number of connectedvehicles approaching an intersection. Each of the connected vehicles104, 106, 108, 110 may be a human-driven connected vehicle or anautonomous connected vehicle. Each of the connected vehicles 104, 106,108, 110 may be an automobile or any other passenger or non-passengervehicle such as, for example, a terrestrial, aquatic, and/or airbornevehicle including, but not limited to, a bus, a scooter, a drone, or abicycle.

The traffic management server 102 may be communicatively coupled to oneor more of the connected vehicles 104, 106, 108, 110. In some examples,the traffic management server 102 may be a road-side unit (RSU)positioned near the intersection 112. In these examples, the system 100may include any number of RSUs spaced along a road near differentintersections such that each RSU covers a different service area. Thatis, as vehicles drive along one or more roads, the vehicles may be inrange of different RSUs at different times such that different RSUsprovide coverage at different locations. Thus, as vehicles drive alongone or more roads, the vehicles may move between coverage areas ofdifferent RSUs.

In other examples, the traffic management server 102 may be another typeof server or computing device and may be positioned remotely from theintersection 112. In some examples, the traffic management server 102may be an edge server. In some examples, the traffic management server102 may be a moving edge server, such as another vehicle. In someexamples, the traffic management server 102 may be a cloud-based server.

As connected vehicles approach the intersection 112, the connectedvehicles may transmit driving data to the traffic management server 102.The driving data transmitted by a connected vehicle may include aposition and speed of the connected vehicle. In some examples, thedriving data transmitted by a connected vehicle may also include otherinformation such as an acceleration or desired acceleration of thevehicle and a planned trajectory of the vehicle.

After receiving driving data from a plurality of connected vehicles, thetraffic management server 102 may schedule the connected vehicles tocross the intersection 112 in a particular order based on the drivingdata, using the techniques described herein. The traffic managementserver 102 may the transmit the order scheduled for the vehicles tocross the intersection and the vehicles may adjust their drivingbehavior to cross the intersection in the appropriate order as scheduledby the traffic management server 102.

FIG. 2 depicts a vehicle system 200 that may be included in each of theconnected vehicles 104, 106, 108, 110 of FIG. 1. The vehicle system 200includes one or more processors 202, a communication path 204, one ormore memory modules 206, a satellite antenna 208, one or more vehiclesensors 210, a network interface hardware 212, and a data storagecomponent 214, the details of which will be set forth in the followingparagraphs. The vehicle system 200 may be included in a human-drivenconnected vehicle and in an autonomous connected vehicle.

Each of the one or more processors 202 may be any device capable ofexecuting machine readable and executable instructions. Accordingly,each of the one or more processors 202 may be a controller, anintegrated circuit, a microchip, a computer, or any other computingdevice. The one or more processors 202 are coupled to a communicationpath 204 that provides signal interconnectivity between various modulesof the system. Accordingly, the communication path 204 maycommunicatively couple any number of processors 202 with one another,and allow the modules coupled to the communication path 204 to operatein a distributed computing environment. Specifically, each of themodules may operate as a node that may send and/or receive data. As usedherein, the term “communicatively coupled” means that coupled componentsare capable of exchanging data signals with one another such as, forexample, electrical signals via conductive medium, electromagneticsignals via air, optical signals via optical waveguides, and the like.

Accordingly, the communication path 204 may be formed from any mediumthat is capable of transmitting a signal such as, for example,conductive wires, conductive traces, optical waveguides, or the like. Insome embodiments, the communication path 204 may facilitate thetransmission of wireless signals, such as WiFi, Bluetooth®, Near FieldCommunication (NFC) and the like. Moreover, the communication path 204may be formed from a combination of mediums capable of transmittingsignals. In one embodiment, the communication path 204 comprises acombination of conductive traces, conductive wires, connectors, andbuses that cooperate to permit the transmission of electrical datasignals to components such as processors, memories, sensors, inputdevices, output devices, and communication devices. Accordingly, thecommunication path 204 may comprise a vehicle bus, such as for example aLIN bus, a CAN bus, a VAN bus, and the like. Additionally, it is notedthat the term “signal” means a waveform (e.g., electrical, optical,magnetic, mechanical or electromagnetic), such as DC, AC,sinusoidal-wave, triangular-wave, square-wave, vibration, and the like,capable of traveling through a medium.

The vehicle system 200 includes one or more memory modules 206 coupledto the communication path 204. The one or more memory modules 206 maycomprise RAM, ROM, flash memories, hard drives, or any device capable ofstoring machine readable and executable instructions such that themachine readable and executable instructions can be accessed by the oneor more processors 202. The machine readable and executable instructionsmay comprise logic or algorithm(s) written in any programming languageof any generation (e.g., 1GL, 2GL, 3GL, 4GL, or 5GL) such as, forexample, machine language that may be directly executed by theprocessor, or assembly language, object-oriented programming (OOP),scripting languages, microcode, etc., that may be compiled or assembledinto machine readable and executable instructions and stored on the oneor more memory modules 206. Alternatively, the machine readable andexecutable instructions may be written in a hardware descriptionlanguage (HDL), such as logic implemented via either afield-programmable gate array (FPGA) configuration or anapplication-specific integrated circuit (ASIC), or their equivalents.Accordingly, the methods described herein may be implemented in anyconventional computer programming language, as pre-programmed hardwareelements, or as a combination of hardware and software components.

Referring still to FIG. 2, the vehicle system 200 comprises a satelliteantenna 208 coupled to the communication path 204 such that thecommunication path 204 communicatively couples the satellite antenna 208to other modules of the vehicle system 200. The satellite antenna 208 isconfigured to receive signals from global positioning system satellites.Specifically, in one embodiment, the satellite antenna 208 includes oneor more conductive elements that interact with electromagnetic signalstransmitted by global positioning system satellites. The received signalis transformed into a data signal indicative of the location (e.g.,latitude and longitude) of the satellite antenna 208, and consequently,the location of the vehicle containing the vehicle system 200.

The vehicle system 200 comprises one or more vehicle sensors 210. Eachof the one or more vehicle sensors 210 is coupled to the communicationpath 204 and communicatively coupled to the one or more processors 202.The one or more sensors 210 may include, but are not limited to, LiDARsensors, RADAR sensors, optical sensors (e.g., cameras, laser sensors,proximity sensors, location sensors (e.g., GPS modules)), and the like.In embodiments, the sensors 210 may monitor the surroundings of thevehicle and may detect other vehicles on the road. For autonomousvehicles, the vehicle system 200 may include an autonomous drivingmodule and the data gathered by the sensors 210 may be used by theautonomous driving module to autonomously navigate the vehicle.

Still referring to FIG. 2, the vehicle system 200 comprises networkinterface hardware 212 for communicatively coupling the vehicle system200 to the traffic management server 102 and/or another vehicle system.The network interface hardware 212 can be communicatively coupled to thecommunication path 204 and can be any device capable of transmittingand/or receiving data via a network. Accordingly, the network interfacehardware 212 can include a communication transceiver for sending and/orreceiving any wired or wireless communication. For example, the networkinterface hardware 212 may include an antenna, a modem, LAN port, Wi-Ficard, WiMax card, mobile communications hardware, near-fieldcommunication hardware, satellite communication hardware and/or anywired or wireless hardware for communicating with other networks and/ordevices. In one embodiment, the network interface hardware 212 includeshardware configured to operate in accordance with the Bluetooth®wireless communication protocol. In embodiments, the network interfacehardware 212 of the vehicle system 200 may transmit driving data (e.g.,vehicle position and speed) to the traffic management server 102.

Still referring to FIG. 2, the vehicle system 200 comprises a datastorage component 214. The data storage component 214 may store dataused by various components of the vehicle system 200. In addition, thedata storage component 214 may store data gathered by the sensors 210.

The vehicle system 200 may also include an interface. The interface mayallow for data to be presented to a human driver and for data or otherinformation to be input by the driver. For example, the interface mayinclude a screen to display information to a driver, speakers to presentaudio information to the driver, and a touch screen that may be used bythe driver to input information. In other examples, the vehicle system200 may include other types of interfaces.

In some embodiments, the vehicle system 200 may be communicativelycoupled to the traffic management server 102 by a network. In oneembodiment, the network may include one or more computer networks (e.g.,a personal area network, a local area network, or a wide area network),cellular networks, satellite networks and/or a global positioning systemand combinations thereof. Accordingly, the vehicle system 200 can becommunicatively coupled to the network via a wide area network, via alocal area network, via a personal area network, via a cellular network,via a satellite network, etc. Suitable local area networks may includewired Ethernet and/or wireless technologies such as, for example,wireless fidelity (Wi-Fi). Suitable personal area networks may includewireless technologies such as, for example, IrDA, Bluetooth®, WirelessUSB, Z-Wave, ZigBee, and/or other near field communication protocols.Suitable cellular networks include, but are not limited to, technologiessuch as LTE, WiMAX, UMTS, CDMA, and GSM.

Now referring to FIG. 3, the traffic management server 102 comprises oneor more processors 302, one or more memory modules 304, networkinterface hardware 306, and a communication path 308. The one or moreprocessors 302 may be a controller, an integrated circuit, a microchip,a computer, or any other computing device. The one or more memorymodules 304 may comprise RAM, ROM, flash memories, hard drives, or anydevice capable of storing machine readable and executable instructionssuch that the machine readable and executable instructions can beaccessed by the one or more processors 302.

The network interface hardware 306 can be communicatively coupled to thecommunication path 308 and can be any device capable of transmittingand/or receiving data via a network. Accordingly, the network interfacehardware 306 can include a communication transceiver for sending and/orreceiving any wired or wireless communication. For example, the networkinterface hardware 306 may include an antenna, a modem, LAN port, Wi-Ficard, WiMax card, mobile communications hardware, near-fieldcommunication hardware, satellite communication hardware and/or anywired or wireless hardware for communicating with other networks and/ordevices. In one embodiment, the network interface hardware 306 includeshardware configured to operate in accordance with the Bluetooth®wireless communication protocol. The network interface hardware 306 ofthe traffic management server 102 may transmit and receive data to andfrom connected vehicles.

The one or more memory modules 304 include a database 312, a drivingdata reception module 314, a conflict path determination module 316, anETA determination module 318, a vehicle scheduling module 320, and avehicle schedule transmission module 322. Each of the database 312, thedriving data reception module 314, the conflict path determinationmodule 316, the ETA determination module 318, the vehicle schedulingmodule 320, and the vehicle schedule transmission module 322 may be aprogram module in the form of operating systems, application programmodules, and other program modules stored in one or more memory modules304. In some embodiments, the program module may be stored in a remotestorage device that may communicate with the traffic management server102. In some embodiments, one or more of the database 312, the drivingdata reception module 314, the conflict path determination module 316,the ETA determination module 318, the vehicle scheduling module 320, andthe vehicle schedule transmission module 322 may be stored in the one ormore memory modules 206 of the vehicle system 200 of a vehicle. Such aprogram module may include, but is not limited to, routines,subroutines, programs, objects, components, data structures and the likefor performing specific tasks or executing specific data types as willbe described below.

The database 312 may store driving data received from connectedvehicles. The database 312 may also store other data that may be used bythe memory modules 304 and/or other components of the traffic managementserver 102.

The driving data reception module 314 may receive driving data from oneor more connected vehicles. The driving data received by the drivingdata reception module 314 from a connected vehicle may include aposition and speed of the connected vehicles. In some examples, thedriving data received by the driving data reception module 314 from aconnected vehicle may include other driving data about the connectedvehicle such as an actual or preferred acceleration of the vehicle or aplanned trajectory of the vehicle. The driving data received by thedriving data reception module 314 may be used by the traffic managementserver 102 as disclosed herein.

The conflict path determination module 316 may determine whether apotential conflict path exists between two or more connected vehicles ata non-signalized intersection based on driving data received from theconnected vehicles. When two or more vehicles approach a non-signalizedintersection, there may be a conflict path between the vehicles. Thatis, two vehicles may have planned trajectories that may lead to acollision between the vehicles without modification to a speed ortrajectory of at least one of the vehicles.

FIG. 4 illustrates an example intersection 400 and potential conflictpoints between vehicles. In the example of FIG. 4, a four-waynon-signalized intersection is shown along with potential paths of fourvehicles 402, 404, 406, and 408. As shown in FIG. 4, potential mergingor crossing conflicts may exist between vehicles depending on thetrajectories taken by each of the vehicles 402, 404, 406, 408.

The conflict path determination module 316 may determine whether apotential conflict path exists between two or more connected vehiclesbased on the planned trajectories of the vehicles. That is, the conflictpath determination module 316 may determine whether the plannedtrajectories of two or more connected vehicles are scheduled to crosseach other.

If two or more connected vehicles have planned trajectories such thatthe vehicles will cross an intersection without conflicting with eachother, then the traffic management server 102 need not schedule thevehicles to cross the intersection. Rather, the vehicles can simplycross the intersection along their planned trajectories withoutmodification. However, if the conflict path determination module 316determines that a potential conflict path exists between two or moreconnected vehicles at a non-signalized intersection, then the trafficmanagement server 102 may schedule the vehicles to cross theintersection to avoid any potential conflict, as disclosed herein.

Referring back to FIG. 3, the ETA determination module 318 may determinean estimated time of arrival for one or more connected vehicles toarrive at an intersection or at a conflict point identified by theconflict path determination module 316. The ETA determination module 318may determine an estimated time of arrival based on the driving datareceived from the connected vehicles. As discussed above, the drivingdata received by the traffic management server 102 may include alocation and speed of a connected vehicle. In addition, the trafficmanagement server 102 may also have access to a location of anintersection that a connected vehicle is approaching. In some examples,this may be received from the connected vehicle itself. In someexamples, the location of an intersection may be received from othertraffic infrastructure (e.g., traffic cameras). In other examples, thelocation of an intersection may be stored in the database 312 (e.g.,after receiving data from a map database).

Because the traffic management server 102 knows the location of anintersection and the location of a connected vehicle, the ETAdetermination module 318 may determine a distance d_(i) between aconnected vehicle i and the intersection or the conflict point. Then, insome examples, the ETA determination module 318 may determine anestimated time of arrival t_(i) for the connected vehicle i to reach theintersection or the conflict point based on the distance d_(i) betweenthe vehicle and the intersection or the conflict point and the speedv_(i) of the vehicle. In particular, the ETA determination module 318may determine the estimated time of arrival as

$t_{i} = {\frac{d_{i}}{v_{i}}.}$

In some examples, in addition to considering a speed of connectedvehicles, the ETA determination module 318 may also consider anacceleration of connected vehicles when determining an estimated time ofarrival. In these examples, the ETA determination module 318 assumesthat, as a connected vehicle i approaches an intersection, the vehiclewill accelerate at a rate of a_(i) until reaching the intersection orreaching a speed of v_(lim) (e.g., a speed limit or a desired cruisingspeed of the vehicle). The speed limit may be the speed limit of theroad on which the vehicle is moving. The desired cruising speed may bedetermined based on driving history of the connected vehicle i undersimilar driving circumstances. In some examples, the acceleration ratea_(i) may be a standard rate of acceleration assumed to be used for allvehicles. In other examples, the acceleration rate a_(i) may be anactual or desired rate of acceleration of the particular connectedvehicle. The acceleration rate a_(i) may be determined based on drivinghistory of the connected vehicle i under similar driving circumstances.

Accordingly, the ETA determination module 318 may consider two possiblecases when a connected vehicle approaches an intersection. In the firstcase, the connected vehicle i begins traveling at speed v_(i) andaccelerates at a constant rate a_(i) but reaches the intersection beforereaching the speed v_(lim). In this case, the vehicle has anacceleration profile as shown in FIG. 5A. As shown in FIG. 5A, thevehicle begins with a speed of v_(i) and accelerates with accelerationa_(i) up to a speed of v_(imax) when it reaches the intersection,traveling a distance of d_(i). Thus the total distance traveled is givenby d_(i)=v_(i)t_(i)+½a_(i)t_(i) ² and the time for the connected vehicleto reach the intersection in the first case is given by:

$\begin{matrix}{t_{i} = \frac{{- v_{i}} + \sqrt{v_{i}^{2} + {2a_{i}d_{i}}}}{a_{i}}} & (1)\end{matrix}$

In the second case, the connected vehicle i begins traveling at speedv_(i) and accelerates at a greater constant rate a_(j) and reachesv_(lim), before reaching the intersection. In this case, the vehicle hasan acceleration profile as shown in FIG. 5B. As shown in FIG. 5B, thevehicle begins with a speed of v_(i) and accelerates with accelerationa_(j) until reaching a speed of v_(lim). The vehicle then travels atconstant speed v_(lim) until reaching the intersection. Thus, the timefor the connected vehicle to reach the intersection in the second caseis given by:

$\begin{matrix}{t_{i} = \frac{\left( {v_{\lim} - v_{i}} \right)^{2} + {2a_{j}d_{i}}}{2a_{j}v_{\lim}}} & (2)\end{matrix}$

Thus, the ETA determination module 318 may determine an estimated timefor a connected vehicle to reach an intersection using the techniquesdescribed above. Specifically, one of the two equations above may beused to estimate a time of arrival for a connected vehicle at anintersection depending on whether the distance d_(i) to the intersectionis sufficient for the vehicle i to accelerate from an initial velocityv_(i) to v_(lim) before reaching the intersection. This may provide areasonable estimated time of arrival without considering trafficconditions. However, in some examples, the ETA determination module 318may take additional steps to determine an estimated time of arrival inconsideration of traffic conditions, as described below.

If a connected vehicle i approaches an intersection and there are noother vehicles in front of the connected vehicle, the above twoequations may be used to estimate a time of arrival for the connectedvehicle at the intersection. However, if there is a leading vehicle k infront of the connected vehicle i, then the connected vehicle i cannotarrive at the intersection before the connected vehicle k. As such, theETA determination module 318 may consider an estimated time of arrivalfor the leading vehicle k before determining an estimated arrival timefor the connected vehicle i.

Specifically, the ETA determination module 318 may determine an initialor temporary time of arrival t_(itemp) for the connected vehicle i toarrive at the intersection using equation (1) or (2) above. The ETAdetermination module 318 may then determine an estimated time of arrivalt_(k) for the leading vehicle k to arrive at the intersection (e.g.,using the techniques described above). It may then be assumed that aminimum time of t_(headway) must elapse between when the leading vehiclek arrives at the intersection and when the connected vehicle i arrivesat the intersection. Thus, the ETA determination module 318 may estimatea time of arrival for the connected vehicle i as the later of t_(itemp)and t_(k)+t_(headway). That is, the time for the connected vehicle toreach the intersection may be given by:

t _(i)=max(t _(itemp) ,t _(k) +t _(headway))  (3)

Referring back to FIG. 3, the vehicle scheduling module 320 may schedulea plurality of connected vehicles to cross an intersection in aparticular order based on estimated times of arrival for the connectedvehicles to arrive at the intersection as determined by the ETAdetermination module 318.

In the illustrated example, a connected vehicle may be scheduled tocross an intersection by the vehicle scheduling module 320 once eitherthe estimated time for the connected vehicle to reach the intersectionis less than a predetermined threshold time (e.g., a reservation-triggertime constant) or the estimated distance from the connected vehicle tothe intersection is less than a predetermined distance (e.g., areservation-trigger geo-fence distance). That is, the vehicle schedulingmodule 320 may schedule a connected vehicle to cross an intersectionwhen the vehicle is close enough to the intersection in either distanceor time. However, in some examples, the connected vehicle may bescheduled only when the estimated time for the connected vehicle toreach the intersection is less than a predetermined threshold time. Inother examples, the connected vehicle may be scheduled only when thedistance to the intersection is less than a predetermined thresholddistance.

In the illustrated example, once a connected vehicle is within athreshold time or a threshold distance from an intersection, the vehiclescheduling module 320 may schedule each connected vehicle approachingthe intersection that has a potential conflict path with anotherconnected vehicle, as determined by the conflict path determinationmodule 316. In particular, the vehicle scheduling module 320 mayconsider the estimated time that each connected vehicle having apotential conflict path will arrive at the intersection, as determinedby the ETA determination module 318. The vehicle scheduling module 320may then schedule the connected vehicles to cross the intersection inthe order of their estimated times of arrival at the intersection. Forexample, the vehicle with the earliest time of arrival may be scheduledfirst, the vehicle with the second earliest time of arrival may bescheduled second, and so on.

FIG. 6 shows an example illustration of the vehicles 104, 106, 108, 110of FIG. 1 approaching the intersection 112 that may be scheduled by thevehicle scheduling module 320. As illustrated in FIG. 6, vehicle 104 ispositioned at a distance d1 from the intersection 112 and has anestimated time of arrival at the intersection 112 of t1. Similarly,vehicles 106, 108, and 110 are positioned at respective distances d2,d3, and d4 from the intersection 112 and have respective times ofarrival at the intersection 112 of t2, t3, and t4.

A geo-fence area 600 is shown around the intersection, which maycorrespond to a reservation-trigger geo-fence distance. That is,connected vehicles may be scheduled by the vehicle scheduling module 320when they are within the geo-fence area or when their expected time ofarrival at the intersection is less than a reservation-trigger timeconstant. In the example of FIG. 6, vehicles 104 and 106 are both withinthe geo-fence area 600. The vehicles 108 and 110 are outside of thegeo-fence area 600 but may be scheduled by the vehicle scheduling module320 if their respective times of arrival t3 and t4 to the intersection112 is less than a reservation-trigger time constant.

In the example of FIG. 6, the vehicles 104, 106, 108, 110 haverespective arrival times to the intersection 112 such that t1<t2<t3<t4.Accordingly, the vehicle scheduling module 320 schedules the vehicle 104to cross the intersection 112 first, the vehicle 106 to cross theintersection 112 second, the vehicle 108 to cross the intersection 112third, and the vehicle 110 to cross the intersection 112 fourth, asshown in FIG. 6.

Referring back to FIG. 3, the vehicle schedule transmission module 322may transmit an order of connected vehicles to cross a non-signalizedintersection scheduled by the vehicle scheduling module 320 to theconnected vehicles so scheduled. After receiving the scheduled order ofconnected vehicles to cross the intersection, the connected vehicles mayadjust their driving behavior such that the vehicles cross theintersection in the scheduled order. For example, vehicles scheduled tocross the intersection later than other vehicles may reduce their speedsuch that the earlier scheduled vehicles may cross the intersectionfirst. In some examples, the scheduled connected vehicles maycommunicate with each other to collectively establish driving behaviorfor each vehicle to ensure that the vehicles cross the intersection inthe scheduled order.

In some examples, an autonomous connected vehicle receiving a vehiclescheduling from the vehicle schedule transmission module 322 mayautonomously adjust its driving behavior to cross the intersection asscheduled. In other examples, a human-driven connected vehicle maydisplay instructions to a driver such that the driver may adjust thedriving behavior of the vehicle to cross the intersection as scheduled.

FIG. 7 depicts a flowchart for operating the traffic management server102 of FIGS. 1 and 3. At step 700, the driving data reception module 314receives driving data from one or more connected vehicles. The drivingdata received by the driving data reception module 314 may includepositions and vehicle speeds of connected vehicles. The driving datareceived by the driving data reception module 314 may also includeaccelerations, desired accelerations, and trajectories of connectedvehicles.

At step 702, the conflict path determination module 316 determineswhether two or more connected vehicles have potential conflicting pathsbased on planned trajectories of the connected vehicles. In particular,the conflict path determination module 316 determines whether two ormore connected vehicles have conflicting paths at a non-signalizedintersection. If the conflict path determination module 316 determinesthat there are no conflicting paths between connected vehicles (no atstep 702), then the method of FIG. 7 ends. Alternatively, if theconflict path determination module 316 determines that two or moreconnected vehicles have conflicting paths (yes at step 702), thencontrol passes to step 704.

At step 704, the ETA determination module 318 determines estimated timesthat connected vehicles for which driving data has been received by thedriving data reception module 314 will arrive at an intersection forwhich the conflict path determination module 316 has determined that apotential conflict path exists. In embodiments, the ETA determinationmodule 318 may determine estimated times of arrival based on the drivingdata received by the driving data reception module 314 using equations(1), (2), and (3) and the techniques described above.

At step 706, the vehicle scheduling module 320 determines whether aconnected vehicle for which driving data has been received by thedriving data reception module 314 is close enough to a non-signalizedintersection having a potential conflict path, as determined by theconflict path determination module 316, to be scheduled to cross theintersection. In particular, the vehicle scheduling module 320 maydetermine whether the estimated time that the connected vehicle willarrive at the intersection is less than a reservation-trigger timeconstant or the distance between the connected vehicle and theintersection is less than a reservation-trigger geo-fence distance.

If the vehicle scheduling module 320 determines that the connectedvehicle is not close enough to the intersection to be scheduled (no atstep 706), then the method of FIG. 7 ends. Alternatively, if the vehiclescheduling module 320 determines that the connected vehicle is closeenough to the intersection to be scheduled (yes at step 706), thencontrol passes to step 708. In some examples, connected vehicles onlytransmit data to the traffic management server 102 when their estimatedtime of arrival to an intersection is less than a reservation-triggertime constant or their distance to the intersection is less than areservation-trigger geo-fence distance. In these examples, step 706 maybe removed from the method of FIG. 7.

At step 708, the vehicle scheduling module 320 schedules the connectedvehicles that are close enough to the intersection to be scheduled tocross the intersection in a particular order. The vehicle schedulingmodule 320 may schedule the connected vehicles to cross the intersectionbased on the estimated time of arrival of each vehicle to theintersection.

At step 710, the vehicle schedule transmission module 322 transmits theresults of the vehicle scheduling performed by the vehicle schedulingmodule 320. That is, the vehicle schedule transmission module 322transmits, to the connected vehicles, the determined order that thevehicles should cross the intersection. The connected vehicles mayreceive the transmitted schedule and may adjust their driving behaviorto ensure that the vehicles cross the intersection in the orderscheduled by the traffic management server 102.

The steps of the method of FIG. 7 may be continually performed atperiodic intervals. As such, as new vehicles approach an intersection,the traffic management server 102 may receive updated driving data andmay determine updated times of arrival based on the updated drivingdata. The traffic management server 102 may then update the vehiclescheduling order for connected vehicles to cross the intersection. Inparticular, the traffic management server 102 may update the vehiclescheduling order at periodic intervals of a certain frequency.

The frequency at which the method of FIG. 7 is performed may be adjustedto modify the performance of the traffic management server 102. If thefrequency at which the method of FIG. 7 is performed is relatively high,the order at which connected vehicles are scheduled to cross anintersection may frequently change, which may cause the vehicles toperform erratic driving behavior while adjusting to the changingschedule. However, if the frequency at which the method of FIG. 7 isperformed is relatively low, the order at which connected vehicles arescheduled to cross an intersection may not be adjusted enough to accountfor new vehicles approaching the intersection that should be scheduledahead of previously scheduled vehicles. As such, the frequency ofoperation of the traffic management server 102 may be adjusted tooptimize performance based on these two factors.

FIG. 8 shows simulation results comparing the performance of theproposed method of scheduling connected vehicles to cross anon-signalized intersection to the use of traffic signals atintersections. In particular, FIG. 8 shows a speed vs. distance plot foran ego vehicle traveling through four intersections. In a baselinescenario, a simulation is performed in which each intersection has atraffic light. Alternatively, in a non-signalized scenario, it isassumed that no traffic lights are present at the intersections and themethod disclosed herein is utilized to control traffic flow at theintersections. As shown in FIG. 8, in the baseline scenario, the egovehicle runs into red lights at the first and fourth intersections andmust come to a complete stop. However, in the non-signalized scenario,the ego vehicle maintains a relatively stable speed while travelingthrough all four intersections, without coming to a full stop at anyintersection. Although a higher maximum speed is reached in the baselinescenario, the excessive speed changes required in the baseline scenariosignificantly increase the overall travel time and energy consumption.In one simulation performed with many trials, the non-signalizedscenario resulted in an average of a 20% reduction in travel time and a23.7% reduction in fuel consumption compared to the baseline scenario.As such, the proposed system and method may significantly improvetraffic flow and energy consumption.

It should now be understood that embodiments described herein aredirected to a system and method for scheduling connected vehicles tocross non-signalized intersections. As a connected vehicle approaches anon-signalized intersection, the vehicle may transmit driving data to atraffic management server. The traffic management server may determinewhether a potential conflict path exists with any other connectedvehicles based on the planned trajectories of the connected vehicles. Ifany potential conflict paths exist, the traffic management server maydetermine an estimated time for each connected vehicle having apotential conflict path to arrive at the intersection based on drivingdata associated with each vehicle. The traffic management server maythen schedule the connected vehicles to cross the intersection in aparticular order based on the estimated time of each vehicle arriving atthe intersection. The traffic management server may then transmit thescheduled order to the connected vehicles and the vehicles may adjusttheir driving behavior to ensure that they cross the intersection in thescheduled order.

It is noted that the terms “substantially” and “about” may be utilizedherein to represent the inherent degree of uncertainty that may beattributed to any quantitative comparison, value, measurement, or otherrepresentation. These terms are also utilized herein to represent thedegree by which a quantitative representation may vary from a statedreference without resulting in a change in the basic function of thesubject matter at issue.

While particular embodiments have been illustrated and described herein,it should be understood that various other changes and modifications maybe made without departing from the spirit and scope of the claimedsubject matter. Moreover, although various aspects of the claimedsubject matter have been described herein, such aspects need not beutilized in combination. It is therefore intended that the appendedclaims cover all such changes and modifications that are within thescope of the claimed subject matter.

1. A method comprising: receiving driving data from a plurality ofconnected vehicles approaching an intersection, the driving datacomprising a speed and position of a connected vehicle; determiningestimated times of arrival that each of the connected vehicles willarrive at the intersection based on the driving data; scheduling theconnected vehicles to cross the intersection in a particular order basedon the estimated times of arrival; and transmitting the scheduled orderto the connected vehicles, causing one or more vehicles of the pluralityof connected vehicles to adjust their driving behavior based on thescheduled order.
 2. The method of claim 1, wherein the driving datafurther comprises an acceleration of the connected vehicle.
 3. Themethod of claim 1, wherein the driving data further comprises a desiredacceleration of the connected vehicle.
 4. The method of claim 1, whereinthe driving data further comprises a planned trajectory of the connectedvehicle.
 5. The method of claim 4, further comprising determiningwhether two or more of the connected vehicles have potential conflictingpaths based on the driving data.
 6. The method of claim 1, furthercomprising determining the estimated times of arrival based on a speedlimit of each of roads on which the connected vehicles drive.
 7. Themethod of claim 1, further comprising: determining whether a distancebetween each of the connected vehicles and the intersection is less thana predetermined threshold distance; and scheduling the connectedvehicles to cross the intersection in the particular order based on theestimated times of arrival in response to determining that the distancebetween each of the connected vehicles and the intersection is less thanthe predetermined threshold distance.
 8. The method of claim 1, furthercomprising: determining whether an estimated time of arrival that eachof the connected vehicles will arrive at the intersection is less than apredetermined threshold time; and scheduling the connected vehicles tocross the intersection in the particular order based on the estimatedtimes of arrival in response to determining that the estimated time ofarrival that each of the connected vehicles will arrive at theintersection is less than a predetermined threshold time.
 9. The methodof claim 1, further comprising determining whether a distance betweeneach of the connected vehicles and the intersection is less than apredetermined threshold distance and an estimated time of arrival thateach of the connected vehicles will arrive at the intersection is lessthan a predetermined threshold time; and scheduling the connectedvehicles to cross the intersection in the particular order based on theestimated times of arrival in response to determining that the distancebetween each of the connected vehicles and the intersection is less thanthe predetermined threshold distance and determining that the estimatedtime of arrival that each of the connected vehicles will arrive at theintersection is less than the predetermined threshold distance.
 10. Themethod of claim 1, further comprising: determining an estimated time ofarrival for at least one of the connected vehicles to arrive at theintersection based on second driving data associated with a leadingvehicle positioned in front of the at least one of the connectedvehicles.
 11. The method of claim 1, further comprising: receivingupdated driving data from the plurality of connected vehicles;determining updated estimated times of arrival for the connectedvehicles based on the updated driving data; and at periodic intervals,scheduling the connected vehicles to cross the intersection in anupdated order based on the updated estimated times of arrival.
 12. Aserver comprising a controller configured to: receive driving data froma plurality of connected vehicles approaching an intersection, thedriving data comprising a speed and position of a connected vehicle;determine estimated times of arrival that each of the connected vehicleswill arrive at the intersection based on the driving data; schedule theconnected vehicles to cross the intersection in a particular order basedon the estimated times of arrival; and transmit the scheduled order tothe connected vehicles, causing one or more vehicles of the plurality ofconnected vehicles to adjust their driving behavior based on thescheduled order.
 13. The server of claim 12, wherein the driving datafurther comprises an acceleration of the connected vehicle.
 14. Theserver of claim 12, wherein the driving data further comprises a desiredacceleration of the connected vehicle.
 15. The server of claim 12,wherein the driving data further comprises a planned trajectory of theconnected vehicle.
 16. The server of claim 15, wherein the controller isfurther configured to determine whether two or more of the connectedvehicles have potential conflicting paths based on the driving data. 17.The server of claim 12, wherein the controller is further configured todetermine the estimated times of arrival based on a speed limit of eachof roads on which the connected vehicles drive.
 18. The server of claim12, wherein the controller is further configured to determine whether adistance between each of the connected vehicles and the intersection isless than a predetermined threshold distance or an estimated time ofarrival that each of the connected vehicles will arrive at theintersection is less than a predetermined threshold time; and schedulethe connected vehicles to cross the intersection in the particular orderbased on the estimated times of arrival in response to determining thatthe distance between each of the connected vehicles and the intersectionis less than a predetermined threshold distance or determining that theestimated time of arrival that each of the connected vehicles willarrive at the intersection is less than a predetermined threshold time.19. The server of claim 12, wherein the controller is further configuredto determine an estimated time of arrival for at least one of theconnected vehicles to arrive at the intersection based on second drivingdata associated with a leading vehicle positioned in front of the atleast one of the connected vehicles.
 20. The server of claim 12, whereinthe controller is further configured to: receive updated driving datafrom the plurality of connected vehicles; determine updated estimatedtimes of arrival for the connected vehicles based on the updated drivingdata; and at periodic intervals, schedule the connected vehicles tocross the intersection in an updated order based on the updatedestimated times of arrival.